Genomics faces the daunting challenge of integrating vast amounts of information generated by high- throughput methods into cohesive datasets to predict gene function and pathways. We propose to identify the cell cycle-regulated genes in a non-transformed human mammary epithelial cell (HMEC) line and directly compare the cell cycle-regulated genes to those in transformed HMEC lines. We present a plan to use the cell cycle-regulated gene expression data as a screen to assign functions to uncharacterized cell cycle- regulated genes, and then to experimentally test these computationally generated hypotheses by cell biological methods. To this end, we propose experiments to identify cell cycle-regulated genes in a three different HMEC lines and characterize the cell cycle regulatory circuitry controlled by the FOXM1 transcription factor in perturbation-based time courses and ChlP-on-chip assays. A key aspect of the study is to further develop Bayesian integration methods, as well as new computational tools, to analyze genome- scale data probing cell growth and proliferation with the goal of providing accurate prediction of gene function for uncharacterized genes as well as a more complete description of the known genes. Using the functional predictions as a guide, previously uncharacterized cell cycle-regulated genes will be analyzed by RNA interference and High Content Screening to test the functional predictions. Finally, knockdown of each unknown will be analyzed by DMA microarrays hybridization to provide an additional "functional readout" for phenotype. All of this data will be integrated back into the Bayesian framework in an iterative manner to increase our predictive power. Cell cycle control is a fundamental process of growth and development and it's deregulation leads to many diseases, most notably, cancer. Therefore, the problems addressed in this proposal are important to public health. Identifying the genes that are regulated and their biological function may aid in the development of anti-cancer therapies and biomarkers.